检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Zhiyong Liu, Yazhou School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781665410205

This work presents a new registration method that especially designed for low-overlapping partial point clouds. Based on the assumption that the partial point clouds to be registered belong to the same target, the proposed Mutual a Priori based Completion (MPC) method uses the partial point clouds to be registered as the a priori for each other to expend each individual point clouds. The main challenge of this mutual reference approach is that the partial clouds without spatial alignment cannot provide reliable completion reference for each. Based on the mutual information maximization, a progressive completion structure is developed to make the alignment in the spatial, feature and completion space respectively. Experiments on public datasets show encouraging results. Especially for the ultra-low overlap cases, compared with the state-of-the-art (SOTA) models, the size of overlapping regions can be increased by about 14.0%, and the rotation and translation error can be reduced by 30.8% and 57.7% respectively. © 2024 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GOAL: Generalized Jointly Sparse Linear Discriminant Regression for Feature Extraction

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4959-4971页

作者： Lu, Haoquan Lai, Zhihui Zhang, Junhong Yu, Zhuozhen Wen, Jiajun Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China

Ridge regression (RR)-based methods aim to obtain a low-dimensional subspace for feature extraction. However, the subspace's dimensionality does not exceed the number of data categories, hence compromising its capability of feature representation. Moreover, these methods with L2-norm metric and regularization cannot extract highly robust features from data with corruption. To address these problems, in this article, we propose generalized jointly sparse linear discriminant regression (GOAL), a novel regression method based on joint L2,1-norm and capped-L2-norm, which can integrate sparsity, locality, and discriminability into one model to learn a full-rank robust feature extractor. The sparsely selected discriminative features are robust enough to characterize the decision boundary between classes. Locality is related to manifold structure and Laplacian smoothing, which can enhance the robustness of the model. By using the multinorm metric and regularization regression framework, the proposed method obtains the projection with joint sparsity and guarantees that the rank of the projection matrix will not be limited by the number of classes. An iterative algorithm is proposed to compute the optimal solution. Complexity analysis and proofs of convergence are also given in the article. Experiments on well-known datasets demonstrate our model's superiority and generalization ability. © 2020 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

引用

International Journal of computers and Applications 2024年第9期46卷 702-714页

作者： Al-Rjoob, Ayat M. Ababnah, Ahmad A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. Electrical and Communication Engineering Department German-Jordanian University and Technology Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology Irbid Jordan Computer Engineering Department School of Engineering The University of Jordan Amman Jordan

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ((Formula presented.)) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary

关键词： Cognitive radio

来源：评论

学校读者我要写书评

暂无评论

Spark workflow task scheduling with deadline and privacy constraints in hybrid cloud networks

引用

Soft Computing 2025年第2期29卷 783-801页

作者： Rajput, Kamran Yaseen Xiaoping, Li Lakhan, Abdullah School of Computer Science and Engineering Southeast University Nanjing China School of Economics Innovations and Technology Kristiania University College Oslo Norway Department of Cybersecurity Dawood University of Engineering and Technology Karachi Pakistan

The increasing adoption of hybrid clouds in organizations stems from their ability to bolster private cloud resources with additional public cloud capacity when required. However, scheduling distributed applications, such as workflow tasks, on hybrid cloud resources presents new and intricate challenges. A significant concern revolves around the potential exposure of private data and tasks within third-party, public cloud infrastructures, especially within sensitive domains like healthcare applications. The complexity escalates when considering the selection of resources from multiple cloud providers due to the fluctuating resource computation prices and data transmission costs. This paper presents the Spark Workflow Task Scheduling to Hybrid Cloud (SWSHC) framework, designed to schedule Spark workflows precisely while adhering to deadline and task privacy constraints within a hybrid cloud setting. Our innovative approach encompasses developing and implementing three pivotal components: deadline division, stage order optimization, and task scheduling mechanisms. We segregate the workflow deadline for each stage to bridge the gaps between stages effectively. Additionally, job prioritization is achieved using the maximum rank rule. The proposed solution considers diverse factors, including interval pricing variations, utilization of heterogeneous VM instances, intra- and inter-bandwidth considerations, and the efficient utilization of private cloud resources. Through meticulous calibration of our algorithm and comprehensive experimentation with various realistic workflows, our findings unequivocally demonstrate that SWSHC surpasses existing solutions in the current literature the cost by up to 40–70% in terms of cost efficiency and resource utilization. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

引用

computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

来源：评论

学校读者我要写书评

暂无评论

Global Color-Aware Arbitrary Style Transfer with Discrete Wavelet Transform 20th

Global Color-Aware Arbitrary Style Transfer with Discrete W...

引用

20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Chen, Bochao Tan, Junhao Li, Xiaoqiang Pei, Songwen School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819628292

The purpose of image arbitrary style transfer is to apply a given artistic or photorealistic style to a target content image. While existing methods can effectively transfer style information, the variability in color brightness features between the style and content images can significantly impact the quality of the style transfer. Specifically, traditional arbitrary style transfer relies on an encoder-decoder architecture of spatial-aware neural networks operating in the RGB domain, which typically preserves only local color brightness information and lacks a global color perception compared to the wavelet frequency domain. In this paper, we address the issue of insufficient global color perception in images from the perspective of image enhancement. We propose Global Color-aware Arbitrary Style Transfer with Discrete Wavelet Transform (GCAST), which achieves a balance between the global color characteristics of style images and the local details of content images. Our approach involves an encoder that integrates preliminary feature encoding through cross-layer attention fusion, while the decoder modifies frequency features using a wavelet-based adjustment block. To maintain a balance between content and style in the stylized images, we design a comprehensive loss function that incorporates multiple restoration loss, double style loss, and noise regularization loss. Extensive experiments comparing our method with state-of-the-art techniques demonstrate that our proposed model generates stylized images with superior results in terms of global color perception and human perceptual studies. © IFIP International Federation for Information Processing 2025.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Behavior Based Group Recommendation from Social Media Dataset by Using Deep Learning and Topic Modeling

引用

SN computer science 2024年第6期5卷 1-17页

作者： Mukta, Md. Saddam Hossain Ahmed, Jubaer Raiaan, Mohaimenul Azam Khan Fahad, Nur Mohammad Islam, Muhammad Nazrul Imtiaz, Nafiz Islam, Md. Adnanul Ali, Mohammed Eunus Azam, Sami LUT School of Engineering Sciences Lappeenranta-Lahti University of Technology Department of Computer Science and Engineering United International University Department of Computer Science and Engineering Military Institute of Science and Technology Department of HCC Action Lab Monash University Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Faculty of Science and Technology Charles Darwin University

In this digital era, users frequently share their thoughts, preferences, and ideas through social media, which reflect their Basic Human Values. Basic Human Values (aka values) are the fundamental aspects of human behavior, which define what we consider important, and worth having and pursue them. Existing studies identify the values of individuals from different social network usages such as Facebook and Reddit. However, discovering the similarity (or diversity) of value priorities among the members in a group is important since we can reveal many interesting insights such as finding a set of target customers, identifying the chain of misdeed groups, searching for similar acquaintances in workplaces, etc. In this paper, a graph dataset is compiled using the strongest correlation among the features and then we apply a graph clustering technique to identify a suitable hedonist group (i.e., one dimension of values) for users’ recommendations. Then, we also propose a behavior based (i.e., value) group recommendation technique by analyzing users’ contextual and psychological attributes. Finally, we validate those group members in real life by introducing two hypotheses. In particular, we analyze the tweets of a total of 1140 users collected from Twitter. We obtain a substantial intra-cluster correlation coefficient (ICC) and silhouette clustering coefficient (SCC) scores of 65% and 76%, respectively, among the members in our discovered group. © The Author(s) 2024.

关键词： Graph neural network (GNN) Group recommendation Psychological attributes Social media Topic modeling Twitter Values

来源：评论

学校读者我要写书评

暂无评论

Impulse Synchronization Strategy of Nonlinear Finance Systems

引用

IAENG International Journal of Applied Mathematics 2024年第9期54卷 1814-1819页

作者： Peng, Yang Feng, Yuming School of Mathematical Physics Science Chongqing University of Science and Technology Chongqing401331 China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing404020 China

In recent decades, impulse control has been increasingly applied as a discontinuous control method across diverse domains such as satellite orbit transfers, financial market regulation, chaos synchronization, and communication security. This paper investigates an impulse synchronization strategy specifically tailored for chaotic financial systems. We propose a novel strategy grounded in Lyapunov stability theory and the maximum Lyapunov exponent, designed to synchronize chaotic financial systems effectively. Simulation experiments are conducted to validate the efficacy of our proposed approach. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Lyapunov methods

来源：评论

学校读者我要写书评

暂无评论

Vehicle to Vehicle Communication and Smart Traffic Management Using LoRa 1

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Thangam, S. Thodathara, Harshitha Reddy Aryagopal Sai Jagadeesh, Dumpa Venkata Maddi, Anish Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

The twenty-first century presents a number of urgent challenges, including global warming, and vehicle-to-vehicle (V2V) communication is one of the key components of an energy-efficient and sustainable transportation system. By adding distance sensors, our research elevates vehicle-to-vehicle (V2V) communication to a new level. This enables vehicles to share not only speed and distance information but also real-time instructions based on their proximity to other vehicles. This system of dynamic control minimizes emissions while optimizing fuel consumption. Beyond the efficiency of a single vehicle, our system uses LoRa technology to integrate with traffic light management. Our method, which treats every car as a node, gathers and analyzes data in the cloud to inform judgments about the length of red lights at intersections. This data-driven traffic analysis streamlines traffic, reduces needless stops, gives commuters time back, and improves overall fuel economy. The application of LoRa technology enhances our traffic analysis's accuracy and effectiveness. Because of its long range, LoRa enables wide-ranging and dependable communication between cars and cloud infrastructure. Because of this improved connectivity, traffic signal timings can be promptly and intelligently adjusted to reflect the dynamically changing patterns of vehicular flow. The end result is a precisely calibrated traffic management system that strategically reduces needless stops while simultaneously optimizing traffic flow and overall fuel efficiency, saving commuters valuable time. Our incorporation of LoRa technology is evidence of the revolutionary potential of sophisticated communication protocols in generating thoughtful and flexible responses to modern transportation problems. To put it briefly, our study offers a novel and comprehensive use of vehicle-to-vehicle (V2V) communication, utilizing LoRa and distance sensors to solve energy-saving issues and transform traffic control for a more env

关键词： Traffic signals

来源：评论

学校读者我要写书评

暂无评论

CSOFS: Feature Selection Using Cuckoo Search Optimization Algorithm for Software Fault Detection 4

CSOFS: Feature Selection Using Cuckoo Search Optimization Al...

引用

4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Rath, Pradeep Kumar Mahato, Susmita Bhowmik, Rwiddhi Gourisaria, Mahendra Kumar Das, Himansu Nist Institute of Science & Technology School of Computer Science & Engineering Berhampur India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

The method of identifying and predicting software flaws or problems in software systems involves using historical data. Software fault prediction is a crucial topic of research in the field of software engineering because it enables developers to find probable flaws early on, which can save money and enhance the quality of the programme produced. The study's objective is to determine how well the Cuckoo Search Optimization Algorithm can extract the features that are most important for correctly predicting software flaws. The prediction models were tested and trained using a dataset of software metrics, and the dataset was subjected to a variety of feature selection algorithms. We have performed an analysis by comparing the efficiency of various optimization algorithms namely evolutionary based Genetic Algorithm Feature Selection (GAFS), Differential Evolution Feature Selection (DEFS), and swarm based algorithms like Ant Colony Optimization Feature Selection (ACOFS) and Particle Swarm Optimization Feature Selection (PSOFS) with respect to Cuckoo Search Optimization Algorithm (CSOFS). The results from the output tables for various metrics prove that feature selection using CSO can significantly increase the accuracy of the predictive model, as well as reduce the complexity of the models. This study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient. © 2024 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：